A large independent study found racial disparities embedded in AI-powered hiring tools used at scale, challenging claims of algorithmic neutrality in early-stage screening. The paper “Algorithmic Monocultures in Hiring,” authored by researchers at Stanford University, Chapman University, and Northeastern University, analyzed more than 4 million applications from 3 million applicants across 156 employers screened by the same talent platform vendor, Pymetrics. The researchers reported that more than 25% of Black applicants were directed to positions where the algorithm produces outcomes triggering federal discrimination scrutiny, and argued disparities appear when positions are analyzed individually rather than pooled. Pymetrics previously reported no disparities at legal scrutiny thresholds, but the new study disputes the measurement approach. For universities and colleges, the relevance is direct to campus employment pipelines (internships, graduate hiring, career centers) and to student training on AI literacy and compliance. The findings also raise higher-ed governance questions about vendor evaluation and fairness testing when AI tools influence access to opportunities.